Energy-Efficient Algorithms for Non-preemptive Speed-Scaling. - Vincent Cohen-Addad; Zhentao Li; Claire Mathieu; Ioannis Milis (WAOA, 2014)

The Unreasonable Success of Local Search: Geometric Optimization. - Vincent Cohen-Addad; Claire Mathieu (CoRR, 2014)

Effectiveness of Local Search for Geometric Optimization. - Vincent Cohen-Addad; Claire Mathieu (SoCG, 2015)

A Fixed Parameter Tractable Approximation Scheme for the Optimal Cut Graph of a Surface. - Vincent Cohen-Addad; Arnaud de Mesmay (ESA, 2015)

Diameter and k-Center in Sliding Windows. - Vincent Cohen-Addad; Chris Schwiegelshohn; Christian Sohler (ICALP, 2016)

The Invisible Hand of Dynamic Market Pricing. - Vincent Cohen-Addad; Alon Eden; Michal Feldman; Amos Fiat (EC, 2016)

Approximating connectivity domination in weighted bounded-genus graphs. - Vincent Cohen-Addad; Éric Colin de Verdière; Philip N. Klein; Claire Mathieu; David Meierfrankenfeld (STOC, 2016)

The power of local search for clustering. - Vincent Cohen-Addad; Philip N. Klein; Claire Mathieu (CoRR, 2016)

Algorithmic aspects of switch cographs. - Vincent Cohen-Addad; Michel Habib; Fabien de Montgolfier (Discret. Appl. Math., 2016)

Online Optimization of Smoothed Piecewise Constant Functions. - Vincent Cohen-Addad; Varun Kanade (AISTATS, 2017)

Fast and Compact Exact Distance Oracle for Planar Graphs. - Vincent Cohen-Addad; Søren Dahlgaard; Christian Wulff-Nilsen (FOCS, 2017)

On the Local Structure of Stable Clustering Instances. - Vincent Cohen-Addad; Chris Schwiegelshohn (FOCS, 2017)

Hierarchical Clustering Beyond the Worst-Case. - Vincent Cohen-Addad; Varun Kanade; Frederik Mallmann-Trenn (NIPS, 2017)

One Size Fits All : Effectiveness of Local Search on Structured Data. - Vincent Cohen-Addad; Chris Schwiegelshohn (CoRR, 2017)

A Fast Approximation Scheme for Low-Dimensional k-Means. - Vincent Cohen-Addad (CoRR, 2017)

Balanced power diagrams for redistricting. - Vincent Cohen-Addad; Philip N. Klein; Neal E. Young (CoRR, 2017)

Steinberg's Conjecture is false. - Vincent Cohen-Addad; Michael Hebdige; Daniel Král'; Zhentao Li; Esteban Salgado (J. Comb. Theory B, 2017)

Balanced centroidal power diagrams for redistricting. - Vincent Cohen-Addad; Philip N. Klein; Neal E. Young (SIGSPATIAL/GIS, 2018)

Clustering Redemption-Beyond the Impossibility of Kleinberg's Axioms. - Vincent Cohen-Addad; Varun Kanade; Frederik Mallmann-Trenn (NeurIPS, 2018)

A Fast Approximation Scheme for Low-Dimensional k-Means. - Vincent Cohen-Addad (SODA, 2018)

The Bane of Low-Dimensionality Clustering. - Vincent Cohen-Addad; Arnaud de Mesmay; Eva Rotenberg; Alan Roytman (SODA, 2018)

Fast fencing. - Mikkel Abrahamsen; Anna Adamaszek; Karl Bringmann; Vincent Cohen-Addad; Mehran Mehr; Eva Rotenberg; Alan Roytman; Mikkel Thorup (STOC, 2018)

Instance-Optimality in the Noisy Value-and Comparison-Model - Accept, Accept, Strong Accept: Which Papers get in? - Vincent Cohen-Addad; Frederik Mallmann-Trenn; Claire Mathieu (CoRR, 2018)

Efficient Approximation Schemes for Uniform-Cost Clustering Problems in Planar Graphs. - Vincent Cohen-Addad; Marcin Pilipczuk; Michal Pilipczuk (ESA, 2019)

A Polynomial-Time Approximation Scheme for Facility Location on Planar Graphs. - Vincent Cohen-Addad; Michal Pilipczuk; Marcin Pilipczuk (FOCS, 2019)

Inapproximability of Clustering in Lp Metrics. - Vincent Cohen-Addad; Karthik C. S. 001 (FOCS, 2019)

Near-Linear Time Approximations Schemes for Clustering in Doubling Metrics. - David Saulpic; Vincent Cohen-Addad; Andreas Emil Feldmann (FOCS, 2019)

Tight FPT Approximations for k-Median and k-Means. - Vincent Cohen-Addad; Anupam Gupta 001; Amit Kumar 001; Euiwoong Lee; Jason Li 006 (ICALP, 2019)

On the Fixed-Parameter Tractability of Capacitated Clustering. - Vincent Cohen-Addad; Jason Li 006 (ICALP, 2019)

Subquadratic High-Dimensional Hierarchical Clustering. - Amir Abboud; Vincent Cohen-Addad; Hussein Houdrouge (NeurIPS, 2019)

Fully Dynamic Consistent Facility Location. - Vincent Cohen-Addad; Niklas Hjuler; Nikos Parotsidis; David Saulpic; Chris Schwiegelshohn (NeurIPS, 2019)

Lower bounds for text indexing with mismatches and differences. - Vincent Cohen-Addad; Laurent Feuilloley; Tatiana Starikovskaya (SODA, 2019)

Oblivious dimension reduction for k-means: beyond subspaces and the Johnson-Lindenstrauss lemma. - Luca Becchetti; Marc Bury; Vincent Cohen-Addad; Fabrizio Grandoni 001; Chris Schwiegelshohn (STOC, 2019)

Hierarchical Clustering: Objective Functions and Algorithms. - Vincent Cohen-Addad; Varun Kanade; Frederik Mallmann-Trenn; Claire Mathieu (J. ACM, 2019)

Local Search Yields Approximation Schemes for k-Means and k-Median in Euclidean and Minor-Free Metrics. - Vincent Cohen-Addad; Philip N. Klein; Claire Mathieu (SIAM J. Comput., 2019)

On Light Spanners, Low-treewidth Embeddings and Efficient Traversing in Minor-free Graphs. - Vincent Cohen-Addad; Arnold Filtser; Philip N. Klein; Hung Le 001 (FOCS, 2020)

On Efficient Low Distortion Ultrametric Embedding. - Vincent Cohen-Addad; Karthik C. S. 001; Guillaume Lagarde (ICML, 2020)

On the Power of Louvain in the Stochastic Block Model. - Vincent Cohen-Addad; Adrian Kosowski; Frederik Mallmann-Trenn; David Saulpic (NeurIPS, 2020)

Fast and Accurate $k$-means++ via Rejection Sampling. - Vincent Cohen-Addad; Silvio Lattanzi; Ashkan Norouzi-Fard; Christian Sohler; Ola Svensson (NeurIPS, 2020)

Approximation Schemes for Capacitated Clustering in Doubling Metrics. - Vincent Cohen-Addad (SODA, 2020)

Instance-Optimality in the Noisy Value-and Comparison-Model. - Vincent Cohen-Addad; Frederik Mallmann-Trenn; Claire Mathieu (SODA, 2020)

New hardness results for planar graph problems in p and an algorithm for sparsest cut. - Amir Abboud; Vincent Cohen-Addad; Philip N. Klein (STOC, 2020)

On the computational tractability of a geographic clustering problem arising in redistricting. - Vincent Cohen-Addad; Philip N. Klein; Dániel Marx (CoRR, 2020)

Online k-means Clustering. - Vincent Cohen-Addad; Benjamin Guedj; Varun Kanade; Guy Rom (AISTATS, 2021)

On the Computational Tractability of a Geographic Clustering Problem Arising in Redistricting. - Vincent Cohen-Addad; Philip N. Klein; Dániel Marx; Archer Wheeler; Christopher Wolfram (FORC, 2021)

Correlation Clustering in Constant Many Parallel Rounds. - Vincent Cohen-Addad; Silvio Lattanzi; Slobodan Mitrovic; Ashkan Norouzi-Fard; Nikos Parotsidis; Jakub Tarnawski (ICML, 2021)

Improving Ultrametrics Embeddings Through Coresets. - Vincent Cohen-Addad; Rémi de Joannis de Verclos; Guillaume Lagarde (ICML, 2021)

Parallel and Efficient Hierarchical k-Median Clustering. - Vincent Cohen-Addad; Silvio Lattanzi; Ashkan Norouzi-Fard; Christian Sohler; Ola Svensson (NeurIPS, 2021)

Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (NeurIPS, 2021)

On Approximability of Clustering Problems Without Candidate Centers. - Vincent Cohen-Addad; Karthik C. S. 001; Euiwoong Lee (SODA, 2021)

A quasipolynomial (2 + ε)-approximation for planar sparsest cut. - Vincent Cohen-Addad; Anupam Gupta 001; Philip N. Klein; Jason Li 006 (STOC, 2021)

A new coreset framework for clustering. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (STOC, 2021)

A Quasipolynomial (2+ε)-Approximation for Planar Sparsest Cut. - Vincent Cohen-Addad; Anupam Gupta 001; Philip N. Klein; Jason Li 006 (CoRR, 2021)

A Linear-Time n0.4-Approximation for Longest Common Subsequence. - Karl Bringmann; Vincent Cohen-Addad; Debarati Das 001 (CoRR, 2021)

Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in L_p metrics. - Vincent Cohen-Addad; Karthik C. S. 001; Euiwoong Lee (CoRR, 2021)

Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in $ ell_p$-metrics. - Vincent Cohen-Addad; Karthik C. S. 001; Euiwoong Lee (Electron. Colloquium Comput. Complex., 2021)

Near-linear Time Approximation Schemes for Clustering in Doubling Metrics. - Vincent Cohen-Addad; Andreas Emil Feldmann; David Saulpic (J. ACM, 2021)

Almost Tight Lower Bounds for Hard Cutting Problems in Embedded Graphs. - Vincent Cohen-Addad; Éric Colin de Verdière; Dániel Marx; Arnaud de Mesmay (J. ACM, 2021)

Mitigating COVID-19 outbreaks in workplaces and schools by hybrid telecommuting. - Simon Mauras; Vincent Cohen-Addad; Guillaume Duboc; Max Dupré la Tour; Paolo Frasca; Claire Mathieu; Lulla Opatowski; Laurent Viennot (PLoS Comput. Biol., 2021)

A Near-Linear Approximation Scheme for Multicuts of Embedded Graphs With a Fixed Number of Terminals. - Vincent Cohen-Addad; Éric Colin de Verdière; Arnaud de Mesmay (SIAM J. Comput., 2021)

On Facility Location Problem in the Local Differential Privacy Model. - Vincent Cohen-Addad; Yunus Esencayi; Chenglin Fan; Marco Gaboardi; Shi Li 001; Di Wang 015 (AISTATS, 2022)

Community Recovery in the Degree-Heterogeneous Stochastic Block Model. - Vincent Cohen-Addad; Frederik Mallmann-Trenn; David Saulpic (COLT, 2022)

The Power of Uniform Sampling for Coresets. - Vladimir Braverman; Vincent Cohen-Addad; Shaofeng H.-C. Jiang; Robert Krauthgamer; Chris Schwiegelshohn; Mads Bech Toftrup; Xuan Wu 002 (FOCS, 2022)

Correlation Clustering with Sherali-Adams. - Vincent Cohen-Addad; Euiwoong Lee; Alantha Newman (FOCS, 2022)

Improved Approximation Algorithms and Lower Bounds for Search-Diversification Problems. - Amir Abboud; Vincent Cohen-Addad; Euiwoong Lee; Pasin Manurangsi (ICALP, 2022)

Online and Consistent Correlation Clustering. - Vincent Cohen-Addad; Silvio Lattanzi; Andreas Maggiori; Nikos Parotsidis (ICML, 2022)

Massively Parallel k-Means Clustering for Perturbation Resilient Instances. - Vincent Cohen-Addad; Vahab S. Mirrokni; Peilin Zhong (ICML, 2022)

A 2-Approximation for the Bounded Treewidth Sparsest Cut Problem in FPT Time. - Vincent Cohen-Addad; Tobias Mömke; Victor Verdugo (IPCO, 2022)

Scalable Differentially Private Clustering via Hierarchically Separated Trees. - Vincent Cohen-Addad; Alessandro Epasto; Silvio Lattanzi; Vahab Mirrokni; Andres Muñoz Medina; David Saulpic; Chris Schwiegelshohn; Sergei Vassilvitskii (KDD, 2022)

Near-Optimal Private and Scalable $k$-Clustering. - Vincent Cohen-Addad; Alessandro Epasto; Vahab Mirrokni; Shyam Narayanan; Peilin Zhong (NeurIPS, 2022)

Near-Optimal Correlation Clustering with Privacy. - Vincent Cohen-Addad; Chenglin Fan; Silvio Lattanzi; Slobodan Mitrovic; Ashkan Norouzi-Fard; Nikos Parotsidis; Jakub Tarnawski (NeurIPS, 2022)

Improved Coresets for Euclidean k-Means. - Vincent Cohen-Addad; Kasper Green Larsen; David Saulpic; Chris Schwiegelshohn; Omar Ali Sheikh-Omar (NeurIPS, 2022)

A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees. - Vincent Cohen-Addad; Frederik Mallmann-Trenn; David Saulpic (PODC, 2022)

An Improved Local Search Algorithm for k-Median. - Vincent Cohen-Addad; Anupam Gupta 001; Lunjia Hu; Hoon Oh; David Saulpic (SODA, 2022)

Johnson Coverage Hypothesis: Inapproximability of k-means and k-median in ℓp-metrics. - Vincent Cohen-Addad; Karthik C. S. 001; Euiwoong Lee (SODA, 2022)

Bypassing the surface embedding: approximation schemes for network design in minor-free graphs. - Vincent Cohen-Addad (STOC, 2022)

Improved approximations for Euclidean k-means and k-median, via nested quasi-independent sets. - Vincent Cohen-Addad; Hossein Esfandiari; Vahab S. Mirrokni; Shyam Narayanan (STOC, 2022)

Towards optimal lower bounds for k-median and k-means coresets. - Vincent Cohen-Addad; Kasper Green Larsen; David Saulpic; Chris Schwiegelshohn (STOC, 2022)

Improved Approximations for Euclidean k-means and k-median, via Nested Quasi-Independent Sets. - Vincent Cohen-Addad; Hossein Esfandiari; Vahab S. Mirrokni; Shyam Narayanan (CoRR, 2022)

Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy. - Limor Gultchin; Vincent Cohen-Addad; Sophie Giffard-Roisin; Varun Kanade; Frederik Mallmann-Trenn (CoRR, 2022)

Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. - Vincent Cohen-Addad; Fabrizio Grandoni 001; Euiwoong Lee; Chris Schwiegelshohn (CoRR, 2022)

On Complexity of 1-Center in Various Metrics. - Amir Abboud; MohammadHossein Bateni; Vincent Cohen-Addad; Karthik C. S. 001; Saeed Seddighin (APPROX/RANDOM, 2023)

Handling Correlated Rounding Error via Preclustering: A 1.73-approximation for Correlation Clustering. - Vincent Cohen-Addad; Euiwoong Lee; Shi Li 001; Alantha Newman (FOCS, 2023)

Planar and Minor-Free Metrics Embed into Metrics of Polylogarithmic Treewidth with Expected Multiplicative Distortion Arbitrarily Close to 1. - Vincent Cohen-Addad; Hung Le 001; Marcin Pilipczuk; Michal Pilipczuk (FOCS, 2023)

Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. - Vincent Cohen-Addad; David Saulpic; Chris Schwiegelshohn (FOCS, 2023)

Streaming Euclidean k-median and k-means with o(log n) Space. - Vincent Cohen-Addad; David P. Woodruff; Samson Zhou (FOCS, 2023)

Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. - Jacob Imola; Alessandro Epasto; Mohammad Mahdian; Vincent Cohen-Addad; Vahab Mirrokni (ICML, 2023)

Graph Searching with Predictions. - Siddhartha Banerjee; Vincent Cohen-Addad; Anupam Gupta 001; Zhouzi Li (ITCS, 2023)

Multi-Swap k-Means++. - Lorenzo Beretta 001; Vincent Cohen-Addad; Silvio Lattanzi; Nikos Parotsidis (NeurIPS, 2023)

Private estimation algorithms for stochastic block models and mixture models. - Hongjie Chen 004; Vincent Cohen-Addad; Tommaso d'Orsi; Alessandro Epasto; Jacob Imola; David Steurer; Stefan Tiegel (NeurIPS, 2023)

Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. - Vincent Cohen-Addad; Fabrizio Grandoni 001; Euiwoong Lee; Chris Schwiegelshohn (SODA, 2023)

On the Fine-Grained Complexity of Approximating k-Center in Sparse Graphs. - Amir Abboud; Vincent Cohen-Addad; Euiwoong Lee; Pasin Manurangsi (SOSA, 2023)

Streaming Euclidean MST to a Constant Factor. - Xi Chen 001; Vincent Cohen-Addad; Rajesh Jayaram; Amit Levi; Erik Waingarten (STOC, 2023)

A PTAS for 𝓁0-Low Rank Approximation: Solving Dense CSPs over Reals. - Vincent Cohen-Addad; Chenglin Fan; Suprovat Ghoshal; Euiwoong Lee; Arnaud de Mesmay; Alantha Newman; Tony Chang Wang (CoRR, 2023)

A quasi-polynomial time algorithm for Multi-Dimensional Scaling via LP hierarchies. - Ainesh Bakshi; Vincent Cohen-Addad; Samuel B. Hopkins 001; Rajesh Jayaram; Silvio Lattanzi (CoRR, 2023)

A Linear-Time n0.4-Approximation for Longest Common Subsequence. - Karl Bringmann; Vincent Cohen-Addad; Debarati Das 001 (ACM Trans. Algorithms, 2023)

Special Section on the Fifty-Ninth Annual IEEE Symposium on Foundations of Computer Science (2018). - Elette Boyle; Vincent Cohen-Addad; Alexandra Kolla; Mikkel Thorup (SIAM J. Comput., 2023)

A Scalable Algorithm for Individually Fair k-Means Clustering. - MohammadHossein Bateni; Vincent Cohen-Addad; Alessandro Epasto; Silvio Lattanzi (AISTATS, 2024)

Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval. - Yanfei Chen; Jinsung Yoon; Devendra Singh Sachan; Qingze Wang; Vincent Cohen-Addad; MohammadHossein Bateni; Chen-Yu Lee; Tomas Pfister (EMNLP (Findings), 2024)

Recent Progress on Correlation Clustering: From Local Algorithms to Better Approximation Algorithms and Back (Invited Talk). - Vincent Cohen-Addad (ESA, 2024)

Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds. - Nikhil Bansal 001; Vincent Cohen-Addad; Milind Prabhu; David Saulpic; Chris Schwiegelshohn (FOCS, 2024)

Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. - Kyriakos Axiotis; Vincent Cohen-Addad; Monika Henzinger; Sammy Jerome; Vahab Mirrokni; David Saulpic; David P. Woodruff; Michael Wunder (ICML, 2024)

Dynamic Correlation Clustering in Sublinear Update Time. - Vincent Cohen-Addad; Silvio Lattanzi; Andreas Maggiori; Nikos Parotsidis (ICML, 2024)

Perturb-and-Project: Differentially Private Similarities and Marginals. - Vincent Cohen-Addad; Tommaso d'Orsi; Alessandro Epasto; Vahab Mirrokni; Peilin Zhong (ICML, 2024)

Multi-View Stochastic Block Models. - Vincent Cohen-Addad; Tommaso d'Orsi; Silvio Lattanzi; Rajai Nasser (ICML, 2024)

A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering. - Vincent Cohen-Addad; Tommaso d'Orsi; Aida Mousavifar (ICML, 2024)

Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems. - Vincent Cohen-Addad; Tommaso d'Orsi; Anupam Gupta 001; Euiwoong Lee; Debmalya Panigrahi (NeurIPS, 2024)

A PTAS for ℓ0-Low Rank Approximation: Solving Dense CSPs over Reals. - Vincent Cohen-Addad; Chenglin Fan; Suprovat Ghoshal; Euiwoong Lee; Arnaud de Mesmay; Alantha Newman; Tony Chang Wang (SODA, 2024)

Understanding the Cluster Linear Program for Correlation Clustering. - Nairen Cao; Vincent Cohen-Addad; Euiwoong Lee; Shi Li 001; Alantha Newman; Lukas Vogl (STOC, 2024)

Combinatorial Correlation Clustering. - Vincent Cohen-Addad; David Rasmussen Lolck; Marcin Pilipczuk; Mikkel Thorup; Shuyi Yan; Hanwen Zhang 003 (STOC, 2024)

Max-Cut with ε-Accurate Predictions. - Vincent Cohen-Addad; Tommaso d'Orsi; Anupam Gupta 001; Euiwoong Lee; Debmalya Panigrahi (CoRR, 2024)

Fully Dynamic Correlation Clustering: Breaking 3-Approximation. - Soheil Behnezhad; Moses Charikar; Vincent Cohen-Addad; Alma Ghafari; Weiyun Ma (CoRR, 2024)

Understanding the Cluster LP for Correlation Clustering. - Nairen Cao; Vincent Cohen-Addad; Euiwoong Lee; Shi Li 001; Alantha Newman; Lukas Vogl (CoRR, 2024)

Embedding Planar Graphs into Graphs of Treewidth O(log3 n). - Hsien-Chih Chang; Vincent Cohen-Addad; Jonathan Conroy; Hung Le 001; Marcin Pilipczuk; Michal Pilipczuk (CoRR, 2024)

Fitting Distances by Tree Metrics Minimizing the Total Error within a Constant Factor. - Vincent Cohen-Addad; Debarati Das 001; Evangelos Kipouridis; Nikos Parotsidis; Mikkel Thorup (J. ACM, 2024)

A 2-approximation for the bounded treewidth sparsest cut problem in sfFPT Time. - Vincent Cohen-Addad; Tobias Mömke; Victor Verdugo (Math. Program., 2024)

Fairness of linear regression in decision making. - Vincent Cohen-Addad; Surya Teja Gavva; Karthik C. S. 001; Claire Mathieu; Namrata (Int. J. Data Sci. Anal., 2024)

Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams. - Ainesh Bakshi; Vincent Cohen-Addad; Rajesh Jayaram; Sam Hopkins 001; Silvio Lattanzi (COLT, 2025)

Fair Clustering in the Sliding Window Model. - Vincent Cohen-Addad; Shaofeng H.-C. Jiang; Qiaoyuan Yang; Yubo Zhang; Samson Zhou (ICLR, 2025)

Embedding Planar Graphs into Graphs of Treewidth O (log3 n ). - Hsien-Chih Chang; Vincent Cohen-Addad; Jonathan Conroy; Hung Le 001; Marcin Pilipczuk; Michal Pilipczuk (SODA, 2025)

A Tight VC-Dimension Analysis of Clustering Coresets with Applications. - Vincent Cohen-Addad; Andrew Draganov; Matteo Russo 002; David Saulpic; Chris Schwiegelshohn (SODA, 2025)

Solving the Correlation Cluster LP in Sublinear Time. - Nairen Cao; Vincent Cohen-Addad; Euiwoong Lee; Shi Li 001; David Rasmussen Lolck; Alantha Newman; Mikkel Thorup; Lukas Vogl; Shuyi Yan; Hanwen Zhang 003 (STOC, 2025)

A (2+ε)-Approximation Algorithm for Metric k-Median. - Vincent Cohen-Addad; Fabrizio Grandoni 001; Euiwoong Lee; Chris Schwiegelshohn; Ola Svensson (STOC, 2025)

Almost Optimal PAC Learning for k-Means. - Vincent Cohen-Addad; Silvio Lattanzi; Chris Schwiegelshohn (STOC, 2025)

Fitting Metrics and Ultrametrics with Minimum Disagreements. - Vincent Cohen-Addad; Chenglin Fan; Euiwoong Lee; Arnaud de Mesmay (SIAM J. Comput., 2025)

Scalable Private Partition Selection via Adaptive Weighting. - Justin Y. Chen; Vincent Cohen-Addad; Alessandro Epasto; Morteza Zadimoghaddam (CoRR, 2025)

REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective. - Simon Geisler; Tom Wollschläger; M. H. I. Abdalla; Vincent Cohen-Addad; Johannes Gasteiger; Stephan Günnemann (CoRR, 2025)

The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence. - Tom Wollschläger; Jannes Elstner; Simon Geisler; Vincent Cohen-Addad; Stephan Günnemann; Johannes Gasteiger (CoRR, 2025)

A (2+ ϵ)-Approximation Algorithm for Metric k-Median. - Vincent Cohen-Addad; Fabrizio Grandoni 001; Euiwoong Lee; Chris Schwiegelshohn; Ola Svensson (CoRR, 2025)

Static to Dynamic Correlation Clustering. - Nairen Cao; Vincent Cohen-Addad; Euiwoong Lee; Shi Li 001; David Rasmussen Lolck; Alantha Newman; Mikkel Thorup; Lukas Vogl; Shuyi Yan; Hanwen Zhang 003 (CoRR, 2025)

Less is More: Adaptive Coverage for Synthetic Training Data. - Sasan Tavakkol; Max Springer; Mohammad Hossein Bateni 001; Neslihan Bulut; Vincent Cohen-Addad; MohammadTaghi Hajiaghayi (CoRR, 2025)

Fast, Space-Optimal Streaming Algorithms for Clustering and Subspace Embeddings. - Vincent Cohen-Addad; Liudeng Wang; David P. Woodruff; Samson Zhou (CoRR, 2025)

Efficient Data Selection at Scale via Influence Distillation. - Mahdi Nikdan; Vincent Cohen-Addad; Dan Alistarh; Vahab Mirrokni (CoRR, 2025)

Private Training & Data Generation by Clustering Embeddings. - Felix Zhou 002; Samson Zhou; Vahab Mirrokni; Alessandro Epasto; Vincent Cohen-Addad (CoRR, 2025)

An Efficient Massively Parallel Constant-Factor Approximation Algorithm for the k-Means Problem. - Vincent Cohen-Addad; Fabian Kuhn; Zahra Parsaeian (CoRR, 2025)

Scalable contribution bounding to achieve privacy. - Vincent Cohen-Addad; Alessandro Epasto; Jason Lee; Morteza Zadimoghaddam (CoRR, 2025)

Approximating High-Dimensional Earth Mover's Distance as Fast as Closest Pair. - Lorenzo Beretta 001; Vincent Cohen-Addad; Rajesh Jayaram; Erik Waingarten (CoRR, 2025)

SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification. - Sasan Tavakkol; Lin Chen; Max Springer; Abigail Schantz; Blaz Bratanic; Vincent Cohen-Addad; MohammadHossein Bateni (CoRR, 2025)